1. Field of the Invention
This invention relates generally to data base processing systems and more particularly to a data mining system employing structured expert randomization.
2. Description of the Prior Art
First-generation expert systems are known in the data base processing arts as production systems where the knowledge base and inference engine are disjoint. Second-generation expert systems are improved in the art to include a rudimentary learning capability, which may be implemented by the interpretation of grids or by user query. Third-generation expert systems are further improved to provide for rule base learning through the use of deductive and inductive processes, as may be appreciated with reference to, for example, Rubin et al. [S. H. Rubin, J. Murthy, M. G. Ceruti, M. Milanova, Z. C. Ziegler, and R. J. Rush Jr., “Third Generation Expert Systems,” Proc. 2d Ann. Information Integration and Reuse Conf., pp. 27–34, 2000].
The theory of randomization is also well-known in the mathematical arts and has been improved by many practitioners, including, for example, Chaitin [G. J. Chaitin, “Randomness and Mathematical Proof,” Sci. Amer., vol. 232, no. 5, pp. 47–52, 1975], Rubin [S. H. Rubin, “New Knowledge for Old Using the Crystal Learning Lamp,” Proc. 1993 IEEE Int. Conf. Syst., Man, Cybern., pp. 119–124, 1993], and Zadeh [L. A. Zadeh, “Fuzzy logic=computing with words,” IEEE Trans. Fuzzy Syst., vol. 4, no. 2, pp. 103–111, 1996]. But non of these practitioners suggest the application of randomization theory to the structured learning problems associate with expert systems.
Chaitin and Kolmogorov first published the theory or randomization, which may be appreciated as a consequence of Gödel's Incompleteness Theorem as described by Uspenskii [V. A. Uspenskii, Gödel 's Incompleteness Theorem, Translated from Russian, Moscow: Ves Mir Publishers, 1987]. Essential incompleteness must in principle preclude the construction of any universal knowledge base requiring only a simple referential search. All tractable learning problems must be domain specific according to Lin et al. [J-H. Lin and J. S. Vitter, “Complexity Results on Learning by Neural Nets,” Mach. Learn., vol. 6, no. 3, pp. 211–230, 1991], which implies that the fundamental randomization problem is unsolvable [S. H. Rubin, “Computing with Words,” IEEE Trans. Syst. Man, Cybern., vol. 29, no. 4, pp. 518–524, 1999]. Thus, when production rules are expressed in the form of situation/action, once a rule is found erroneous, it must be corrected through acquisition of a new rule for each correction. This is also referred to as linear learning.
As described by Feigenbaum et al. [E. A. Feigenbaum and P. McCorduck, The Fifth Generation. Reading, Mass.: Addison-Wesley Publishing Co., 1983], solutions to the knowledge acquisition bottleneck are the keys to the creation of intelligent software. Learning how to learn is fundamentally dependent on representing the knowledge in the form of a society of experts. Minsky's proposals led to the development of intelligent agent architectures [M. Minsky, The Society of Mind. New York, N.Y.: Simon and Schuster, Inc., 1987] and affirmed that the representational formalism itself must be included in the definition of domain-specific learning for scalability.
Accordingly, there remains in the art a clearly-felt need for an expert system architecture that may automatically expand the rule base without the concomitant data input burden associated with error correction needed to optimize expert system performance. An expert system that includes learning means for acquiring a rule system that functions as a larger virtual rule system with reduced error probability has, until now, been unavailable in the art. These unresolved problems and deficiencies are clearly felt in the art and are solved by this invention in the manner described below.